class_docstr: Reduce feature map size by factorized pointwise (stride=2).
code: def __init__(self, C_in, C_out, affine=True):
      super().__init__()
      self.relu = nn.ReLU()
      self.conv1 = nn.Conv2d(
          C_in, C_out // 2, 1, stride=2, padding=0, bias=False
      )
      self.conv2 = nn.Conv2d(
          C_in, C_out // 2, 1, stride=2, padding=0, bias=False
      )
      self.bn = nn.BatchNorm2d(C_out, affine=affine)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new FactorizedReduce object from inherited base classes: nn with example: obj = FactorizedReduce(C_in, C_out, affine)